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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 389463, 6 pages
http://dx.doi.org/10.1155/2012/389463
Research Article

Modeling and Representation of Human Hearts for Volumetric Measurement

1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China
3DreamSciTech Consulting, Shenzhen 518054, China

Received 24 July 2011; Accepted 28 August 2011

Academic Editor: Carlo Cattani

Copyright © 2012 Qiu Guan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Y. Chen, J. Zhang, H. Zhang et al., “Myocardial motion analysis for determination of tei-index of human heart,” Sensors, vol. 10, no. 12, pp. 11428–11439, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. D. Moroni, S. Colantonio, O. Salvetti, and M. Salvetti, “Heart deformation pattern analysis through shape modelling,” Pattern Recognition and Image Analysis, vol. 19, no. 2, pp. 262–270, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. X. Huang and D. N. Metaxas, “Metamorphs: deformable shape and appearance models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1444–1459, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. R. I. Ionasec, I. Voigt, B. Georgescu et al., “Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE,” IEEE Transactions on Medical Imaging, vol. 29, no. 9, Article ID 5458068, pp. 1636–1651, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Y. Chen and Q. Guan, “Parametric shape representation by a deformable NURBS model for cardiac functional measurements,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 3, pp. 480–487, 2011.
  6. T. Arts, F. W. Prinzen, T. Delhaas, J. R. Milles, A. C. Rossi, and P. Clarysse, “Mapping displacement and deformation of the heart with local sine-wave modeling,” IEEE Transactions on Medical Imaging, vol. 29, no. 5, Article ID 5437350, pp. 1114–1123, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Yamamuro, E. Tadamura, S. Kubo et al., “Cardiac functional analysis with multi-detector row CT and segmental reconstruction algorithm: comparison with echocardiography, SPECT, and MR imaging,” Radiology, vol. 234, no. 2, pp. 381–390, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Germano, H. Kiat, P. B. Kavanagh et al., “Automatic quantification of ejection fraction from gated myocardial perfusion SPECT,” Journal of Nuclear Medicine, vol. 36, no. 11, pp. 2138–2147, 1995. View at Scopus
  9. C. Lorenz and J. V. Berg, “A comprehensive shape model of the heart,” Medical Image Analysis, vol. 10, no. 4, pp. 657–670, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. M. E. Barley, A. M. Galea, A. A. Armoundas, T. S. Rosbury, and G. B. Hirschman, “Validation of a novel catheter guiding method for the ablative therapy of ventricular tachycardia in a phantom model,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 3, Article ID 4663616, pp. 907–910, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Y. Chen, “Cardiac deformation mechanics from 4D images,” Electronics Letters, vol. 43, no. 11, pp. 609–611, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Luo and P. A. Heng, “LV shape and motion: B-spline-based deformable model and sequential motion decomposition,” IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 3, pp. 430–446, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. O. Ecabert, J. Peters, H. Schramm et al., “Automatic model-based segmentation of the heart in CT images,” IEEE Transactions on Medical Imaging, vol. 27, no. 9, Article ID 4505365, pp. 1189–1202, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Lu, S. Liu, W.-L. Wang, and S. Y. Chen, “Generation of a point distribution model using genetic algorithms,” in Proceedings of the 4th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, Tunisia, March 2007.
  15. T. F. Cootes and C. J. Taylor, “Statistical model of appearance for computer vision,” Tech. Rep., University of Manchester, Manchester, UK, 2004.
  16. A. Andreopoulos and J. K. Tsotsos, “Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI,” Medical Image Analysis, vol. 12, no. 3, pp. 335–357, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Koikkalainen, T. Tolli, K. Lauerma et al., “Methods of artificial enlargement of the training set for statistical shape models,” IEEE Transactions on Medical Imaging, vol. 27, no. 11, Article ID 4591396, pp. 1643–1654, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Izard, B. Jedynak, and C. Stark, “Automatic landmarking of magnetic resonance brain images,” in Proceedings of the SPIE International Symposium on Medical Imaging, vol. 5747, pp. 1329–1340, San Diego, Calif, USA, 2005.
  19. C. Mclntosh and G. Hamarneh, “Genetic algorithm driven statistically deformed models for medical image segmentation,” in Proceedings of the Genetic and Evolutionary Computation Conference, (GECCO '06), Image Processing and Computer Vision, Seattle, Wash, USA, July 2006.
  20. T. T. Jiang, S. Y. Chen, and Y. Xu, “3-D representation and volumetric measurement of human heart from a cylindrical B-spline surface model,” in Proceedings of the International Conference on BioMedical Engineering and Informatics, pp. 765–769, Sanya, Hainan, China, May 2008.
  21. B. Zhang and J. F. M. Molenbroek, “Representation of human head with bi-cubic B-Spline technique based on the laser scanning technique in 3D surface anthropometry,” Applied Ergonomics, vol. 35, no. 5, pp. 459–465, 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Y. Chen and Y. F. Li, “Determination of stripe edge blurring for depth sensing,” IEEE Sensors Journal, vol. 11, no. 2, Article ID 5585653, pp. 389–390, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Y. Chen, H. Tong, Z. Wang, S. Liu, M. Li, and B. Zhang, “Improved generalized belief propagation for vision processing,” Mathematical Problems in Engineering, vol. 2011, Article ID 416963, 12 pages, 2011. View at Publisher · View at Google Scholar
  24. A. I. Veress, W. P. Segars, B. M. W. Tsui, and G. T. Gullberg, “Incorporation of a left ventricle finite element model defining infarction into the XCAT imaging phantom,” IEEE Transactions on Medical Imaging, vol. 30, no. 4, pp. 915–927, 2011.
  25. K. Punithakumar, I. B. Ayed, A. Islam, I. G. Ross, and S. Li, “Tracking endocardial motion via multiple model filtering,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 8, Article ID 5471238, pp. 2001–2010, 2010. View at Publisher · View at Google Scholar · View at Scopus